Case Study: Sprinklr cuts retrieval infrastructure cost 30% with Qdrant
Key results
The challenge
Sprinklr, a leader in unified customer experience management, engages global brands across more than 30 digital channels and needed a scalable vector database to power AI-driven search for RAG applications, FAQ bots, and customer-interaction analysis. Its team ran a comprehensive evaluation to benchmark speed, cost, and developer experience.
The solution
After evaluating Pinecone, Weaviate, and Elasticsearch, Sprinklr adopted Qdrant, starting with 10% of its workloads before scaling up. Quantization and memory-mapping features let the team reduce RAM usage for further cost savings.
“Retrieval is the foundation of all our AI tasks, and Qdrant's resilience and speed have made it an integral part of our system.”
RSRaghav SonavaneAssociate Director of Machine Learning Engineering, Sprinklr
The results, in context
Internal benchmarking showed Qdrant reduced Sprinklr's retrieval infrastructure cost by 30%. In Sprinklr's benchmarks Qdrant delivered a P99 latency of 20ms on 1 million-vector searches, handled up to 250 requests per second versus roughly 100 RPS for Elasticsearch, and required less than 10% of Elasticsearch's incremental indexing time for 100k–1M vectors.